293 citations · 1.5k across the 77 of their papers we have counts for
7 papers · 1 filter
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
Boxin Wang, Hengzhi Pei, Boyuan Pan +3
Adversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models. Thou…
Adversarial Attack Generation Empowered by Min-Max Optimization
Jingkang Wang, Tianyun Zhang, Sijia Liu +4
The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing ad…
Approximately Maximizing the Broker's Profit in a Two-sided Market
Jing Chen, Bo Li, Yingkai Li
We study how to maximize the broker's (expected) profit in a two-sided market, where she buys items from a set of sellers and resells them to a set of buyers. Each seller has a sin…
Maximin-Aware Allocations of Indivisible Goods
Hau Chan, Jing Chen, Bo Li +1
We study envy-free allocations of indivisible goods to agents in settings where each agent is unaware of the goods allocated to other agents. In particular, we propose the maximin…
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning
Xinlei Pan, Weiyao Wang, Xiaoshuai Zhang +3
Machine learning has been widely applied to various applications, some of which involve training with privacy-sensitive data. A modest number of data breaches have been studied, in…
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
Shawn Shan, Emily Wenger, Bolun Wang +3
Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or cost…